SlideShare a Scribd company logo
1 of 29
Logic

Is the study of reasoning; it is specifically concerned
with whether reasoning is correct.
It focuses on the relationship among statements as
opposed to the content of any particular statement.
Proposition
A sentence that is either true or false, but not both.
It is typically expressed as a declarative sentence (as
opposed to a question, command, etc.).
Propositions are the basic building blocks of any
theory of logic.
Which sentences are proposition?
1. The only positive integers that divide 7 are 1 and 7
itself.
2. Alfred Hitchcock won an Academy Award in 1940 for
directing “Rebecca.”
3. For every positive integer n, there is a prime number
larger than n.
4. Earth is the only planet in the universe that contains
life.
5. Buy two tickets to the “Never Say Never” concert.
We will use variables, such as p, q, and r, to represent
propositions.
Let p and q be propositions.
The conjunction of p and q, denoted p ʌ q, is the
proposition
p and q
The disjunction of p and q, denoted p v q, is the
proposition
p or q
example:
if

p: It is raining,
q: It is cold,

then the conjunction of p and q is
p ʌ q: It is raining and It is cold.
The disjunction of p and q is
p v q: It is raining or it is cold.
The inclusive-or of propositions p and q is true if both p
and q are true.
The exclusive-or that defines p exor q to be false if both
p and q are true.
Exclusive-or
p
T
T
F
F

q
T
F
T
F

p exor q
F
T
T
F
Truth tables
The truth values of propositions such as
conjunctions and disjunctions can be described by truth
tables.
Conjunction
Disjunction
pʌ
q
T
T
T
T
F
F
F
T
F
F
F
F
The binary operator on a set
elements in X an element of X.
p

q

p
T
T
F
F

q
T
F
T
F

pvq
T
T
T
F

X assigns to each pair of

ʌ and v are both binary operator on propositions.
The negation of p, denoted by ¬p, is the proposition
not p
example:

p: Paris is the capital of England.
¬p: Paris is not the capital of England.
Negation
p
T
F

¬p
F
T

A unary operator on a set X assigns to each element in
X an element of X.

¬ is a unary operator on propositions.
Operator Precedence
In expressions involving some or all of the
operators ¬, ʌ, and v, in the absence of parentheses, we
first evaluate ¬, then ʌ, and then v.
example: Given that proposition p is false, proposition
q is true, and proposition r is false, determine whether
the proposition
¬p v q ʌ r
is true or false.
Exercises:
I.Given that proposition p is false, proposition q is true,
and proposition r is false, determine whether each
proposition is true or false.
1. p v q
3. ¬p v q
5. ¬(p v q) ʌ (¬p v r)
II.

2. ¬p v ¬q
4. ¬p v ¬(q ʌ r)
6. (p v ¬r) ʌ ¬[(q v r) v ¬(r v p)]

Write the truth table of each proposition.

7. p ʌ ¬q
9. (p v q) ʌ ¬p
11. (p ʌ q) v (¬p v q)
13. ¬(p ʌ q) v (¬q v r)

8. (¬p v ¬q) v p
10. (p ʌ q) ʌ ¬p
12. ¬(p ʌ q) v (r ʌ ¬p)
14. (p ʌ q) ʌ ¬(r v p)
III. Represent the proposition symbolically by letting
p: There is a storm.
q: It is raining.
15. There is no storm.
16. There is a storm and it is raining.
17. There is a storm, and it is not raining.
18. There is no storm and it is not raining.
19. Either there is a storm or it is raining.
20. Either there is a storm or it is raining, but there is
no storm.
Conditionals
The implication (or conditional) p → q, is the
proposition “if p, then q”, that is false when p is true
and q is false and true otherwise. In this implication
p is called the hypothesis (or antecedent or
premise) and q is called the conclusion (or
consequence).
Implication
p
T
T
F
F

q
T
F
T
F

p →q
T
F
T
T
Other ways of expressing implication:
“p implies q”
“p is sufficient for q”
“when p then q”
“q if p”
“if p, q”
“q whenever p”
“p only if q”
“q is necessary for p”
Example: Rewrite the given implication in other
forms.
If it snows, then the streets are slick.
1.The streets are slick if it snows.
2.Snowy weather implies the streets are slick.
3.When it snows, then the streets are slick.
4.It snows only if the streets are slick.
Related Conditionals
The converse of the implication “if p, then q”, is the
implication “if q, then p.” That is, the converse of p→q
is q→p.
The inverse of the implication “if p, then q”, is the
implication “if not p, then not q” that results when p
and q are replaced by their negations; that is, the
inverse of p→q is ¬p→¬q
The contrapositive of the implication “if p, then q”, is
the implication “if not q, then not p.” That is, the
contrapositive of p→q is ¬q→¬p.
Example: Find the converse, inverse,
contrapositive of the given conditional.

and

If it rains, then I buy a new umbrella.
Converse:
If I buy a new umbrella, then it is raining.
Inverse:
If it does not rain, then I do not buy a new umbrella.
Contrapositive:
If I do not buy a new umbrella, then it is not raining.
The biconditional p↔q is the proposition that is
true when p and q have the same truth values and is
false otherwise.
“p if and only if q”
“p is necessary and sufficient for q”
“if p then q, and conversely”
Biconditional
p
T
T
F
F

q
T
F
T
F

p ↔q
T
F
F
T
Propositional Equivalences
Tautology is a compound proposition that is always
true, no matter what the truth values of the
propositions that occur in it.
Contradiction is a compound proposition that is
always false.
Contingency is a proposition that is neither a
tautology nor a contradiction.
Example of Tautology and Contradiction
p
T
F

¬p
F
T

p v ¬p
T
T

p ʌ ¬p
F
F
Logical Equivalence
Compound propositions that have the same truth
values in all positive cases are called logically
equivalent.
The propositions p and q are called logically
equivalent if p↔q is a tautology. The notation

p⇔q

denotes that p and q are logically equivalent.
Ex.
Show that the propositions p v (q ʌ r) and (p v q) ʌ
(p v r) are logically equivalent.
Table
Exercises:
I.Let A: “It is snowing”
B: “The roofs are white”
C: “The streets are slick”
D: “The trees are covered with ice”
Write the following in symbolic notation.
1. If it is snowing, then the trees are covered
with ice.
2. If it is not snowing, then the roofs are not
white.
3. If the streets are not slick, then it is not
snowing.
4. If the streets are slick, then the trees are not
covered with ice.
II. State the converse, inverse, and contrapositive of
each of the following conditionals.
1. If a triangle is a right triangle, then one angle has
a measure of 900.
2. If a number is a prime, then it is odd.
3. If two lines are parallel, then the alternate interior
angles are equal.
4. If Joyce is smiling, then she is happy.
III. Using truth tables, determine whether the
following pairs of statements are equivalent.
1. p v q; ¬p→q
2. ¬(p ʌ q); ¬p v ¬q
3. ¬(p v q); ¬p ʌ ¬q
4. p→q; ¬p→¬q
IV. Using truth tables, determine if the argument is
tautology, contradiction or contingency.
1.¬p → (p→q)
2.[p ʌ (p→q)] → ¬q
3.p ʌ ¬p
4.[(p→q) ʌ (q→r)] → (p→r)
5.[(p v q) ʌ (p→r) ʌ (q→r)] → r
6.(p→q) ʌ (p ʌ ¬q)
Quantifiers, Venn Diagrams, and Valid Arguments
Quantifiers give information about “how many” in the
statements in which they occur.
ex. all, some, no
Quantified Statements are statements involving
quantifiers.
ex. Some women have red hair.
All bananas are yellow.
No professors are bald-headed.
Universal Quantifiers ( ∀ )
The words all, every, and each are called universal
quantifiers because when these words are added to
an open sentence to make it a statement, the
sentence must be true in all possible instances in
order for the statement to be true.

∀xP(x)

“for all x P(x)” or “for every x P(x)

examples:
All prime numbers greater than 2 are odd.
Every automobile pollutes the atmosphere.
For each number x, x + 3 = 3 + x.
Existential Quantifiers ( ∃ )
Other quantified statements are intended to indicate
that there exists at least one case in which the
statement is true. Such statements generally involve
one of the existential quantifiers: some, there exist,
or there exists at least one.

∃xP(x)

“there is an x such that P(x)”
or “there is at least one x such that P(x)

examples:
Some men have black hair.
There exist students who do not work hard.
There exists at least one student who does not
work hard.
Venn Diagrams
A diagram in which the interiors of simple closed
curves such as circles are used to represent
collections of objects (or sets).
It provide geometrical representations of the
relationships indicated by statements involving
quantifiers.
ex. Draw Venn diagrams for the statements.
1.All dogs (D) are animals (A).
2.Some kindergarten students (K) are able to read
(R).
3.No cat (C) is a dog (D).
Valid Arguments
The argument is valid if in all cases where the
premises are true, the conclusion is true.
Ex. Consider the following two arguments. Use
Venn diagrams to determine whether either is valid.
a)All leghorns are chickens.
All chickens are fowls.
Therefore, all leghorns are fowls.
b)All leghorns are chickens.
All chickens are fowls.
Therefore, all fowls are leghorns.
If, in some case, the conjunction of the premises is
true and the conclusion is false, then the argument is
invalid. Invalid arguments are sometimes called
fallacies.
Theorem is a statement that can shown to be true.
Proof is a sequence of statements that form an
argument to demonstrate that a theorem is true.
Axioms or Postulates are the underlying
assumptions about mathematical structures, the
hypotheses of the theorem to be proved, and
previously proved theorem.
Lemma is a simple theorem used in the proof of
other theorems.
Corollary is a proposition that can be established
directly from a theorem that has been proved.
Conjecture is a statement whose truth value is
unknown. When a proof of a conjecture is found, the
conjecture becomes a theorem. Many times
conjectures are shown to be false, so they are not
theorems.
Rules of Inference which are means used to draw
conclusions from other assertions, tie together the
steps of a proof.
Rules of Inference
Rule

Tautology

Name

p
∴p∨q

p → ( p ∨ q)

Addition

p∧q
∴p

( p ∧ q) → p

Simplification

p
q
∴p∧q

( ( p) ∧ ( q) ) → ( p ∧ q)

Conjuction
Rule

p
p→q
∴q
¬q
p→q
∴ ¬p
p→q
q→r
∴p→r
p∨q
¬p
∴q

Tautology

[ p ∧ ( p → q)] → q
[ ¬q ∧ ( p → q ) ] → ¬p

Name
Modus ponens

Modus tollens

[( p → q) ∧ ( q → r )] → ( p → r )
Hypothetical syllogisms or chain rule

[ ( p ∨ q ) ∧ ¬p ] → q

Disjunctive
syllogisms

More Related Content

What's hot (20)

Rules of inference
Rules of inferenceRules of inference
Rules of inference
 
1.3.2 Conditional Statements
1.3.2 Conditional Statements1.3.2 Conditional Statements
1.3.2 Conditional Statements
 
Truth table
Truth tableTruth table
Truth table
 
Formal Logic - Lesson 4 - Tautology, Contradiction and Contingency
Formal Logic - Lesson 4 - Tautology, Contradiction and ContingencyFormal Logic - Lesson 4 - Tautology, Contradiction and Contingency
Formal Logic - Lesson 4 - Tautology, Contradiction and Contingency
 
Propositional logic
Propositional logicPropositional logic
Propositional logic
 
Prpositional2
Prpositional2Prpositional2
Prpositional2
 
Truth tables
Truth tablesTruth tables
Truth tables
 
Discrete mathematics
Discrete mathematicsDiscrete mathematics
Discrete mathematics
 
LECTURE 2: PROPOSITIONAL EQUIVALENCES
LECTURE 2: PROPOSITIONAL EQUIVALENCESLECTURE 2: PROPOSITIONAL EQUIVALENCES
LECTURE 2: PROPOSITIONAL EQUIVALENCES
 
Proposition
PropositionProposition
Proposition
 
Truth table
Truth tableTruth table
Truth table
 
Logic (PROPOSITIONS)
Logic (PROPOSITIONS)Logic (PROPOSITIONS)
Logic (PROPOSITIONS)
 
Logic Statements:Conditional statements
Logic Statements:Conditional statementsLogic Statements:Conditional statements
Logic Statements:Conditional statements
 
CMSC 56 | Lecture 5: Proofs Methods and Strategy
CMSC 56 | Lecture 5: Proofs Methods and StrategyCMSC 56 | Lecture 5: Proofs Methods and Strategy
CMSC 56 | Lecture 5: Proofs Methods and Strategy
 
Proposition (Logic)
Proposition (Logic)Proposition (Logic)
Proposition (Logic)
 
Formal Logic - Lesson 5 - Logical Equivalence
Formal Logic - Lesson 5 - Logical EquivalenceFormal Logic - Lesson 5 - Logical Equivalence
Formal Logic - Lesson 5 - Logical Equivalence
 
Translating English to Propositional Logic
Translating English to Propositional LogicTranslating English to Propositional Logic
Translating English to Propositional Logic
 
Logic (slides)
Logic (slides)Logic (slides)
Logic (slides)
 
Chapter1p1
Chapter1p1Chapter1p1
Chapter1p1
 
Discrete math Truth Table
Discrete math Truth TableDiscrete math Truth Table
Discrete math Truth Table
 

Viewers also liked

Mathematical Logic - Part 1
Mathematical Logic - Part 1Mathematical Logic - Part 1
Mathematical Logic - Part 1blaircomp2003
 
Nicole trunfio supermodel
Nicole trunfio supermodelNicole trunfio supermodel
Nicole trunfio supermodelcherrysmith
 
Second evaltuation q1
Second evaltuation q1Second evaltuation q1
Second evaltuation q1Ashley4510
 
Step By Step - Front Cover
Step By Step - Front CoverStep By Step - Front Cover
Step By Step - Front CoverAshley4510
 
제4회 SK APPJAM 앱등이팀 - 커프리
제4회 SK APPJAM 앱등이팀 - 커프리제4회 SK APPJAM 앱등이팀 - 커프리
제4회 SK APPJAM 앱등이팀 - 커프리상현 양
 
Second evaltuation q1
Second evaltuation q1Second evaltuation q1
Second evaltuation q1Ashley4510
 
Leaders of the new world of finance
Leaders of the new world of finance Leaders of the new world of finance
Leaders of the new world of finance RippleIsrael
 
2kyuu matome bunpou
2kyuu matome bunpou2kyuu matome bunpou
2kyuu matome bunpouThành Công
 
Masper - The India's Leading Home Furnishing Store
Masper - The India's Leading Home Furnishing StoreMasper - The India's Leading Home Furnishing Store
Masper - The India's Leading Home Furnishing Storemasparindia
 
2015 하반기 AppJam
2015 하반기 AppJam 2015 하반기 AppJam
2015 하반기 AppJam 상현 양
 
Verslag humanistisch lab 20 oktober HV MiddenHolland
Verslag humanistisch lab 20 oktober HV MiddenHollandVerslag humanistisch lab 20 oktober HV MiddenHolland
Verslag humanistisch lab 20 oktober HV MiddenHollandHVMiddenHolland
 
Karya Ilmiah - Silvana Evi Linda
Karya Ilmiah - Silvana Evi LindaKarya Ilmiah - Silvana Evi Linda
Karya Ilmiah - Silvana Evi Lindafikri_muh
 
Uses & gratifications
Uses & gratifications Uses & gratifications
Uses & gratifications Ashley4510
 
тема 2 аппаратное обеспечение ит
тема 2   аппаратное обеспечение иттема 2   аппаратное обеспечение ит
тема 2 аппаратное обеспечение итfelbnjhbz
 
Evaluation Question 1
Evaluation Question 1Evaluation Question 1
Evaluation Question 1Ashley4510
 

Viewers also liked (20)

Mathematical Logic - Part 1
Mathematical Logic - Part 1Mathematical Logic - Part 1
Mathematical Logic - Part 1
 
Nicole trunfio supermodel
Nicole trunfio supermodelNicole trunfio supermodel
Nicole trunfio supermodel
 
Introduction to Swift 2
Introduction to Swift 2Introduction to Swift 2
Introduction to Swift 2
 
Second evaltuation q1
Second evaltuation q1Second evaltuation q1
Second evaltuation q1
 
Museum Object Pest & Mold Control Methods in Storage-JenniferHein Conservation
Museum Object Pest & Mold Control Methods in Storage-JenniferHein ConservationMuseum Object Pest & Mold Control Methods in Storage-JenniferHein Conservation
Museum Object Pest & Mold Control Methods in Storage-JenniferHein Conservation
 
Art Photos- Clouds across the usa
Art Photos- Clouds across the usaArt Photos- Clouds across the usa
Art Photos- Clouds across the usa
 
Step By Step - Front Cover
Step By Step - Front CoverStep By Step - Front Cover
Step By Step - Front Cover
 
제4회 SK APPJAM 앱등이팀 - 커프리
제4회 SK APPJAM 앱등이팀 - 커프리제4회 SK APPJAM 앱등이팀 - 커프리
제4회 SK APPJAM 앱등이팀 - 커프리
 
Second evaltuation q1
Second evaltuation q1Second evaltuation q1
Second evaltuation q1
 
Leaders of the new world of finance
Leaders of the new world of finance Leaders of the new world of finance
Leaders of the new world of finance
 
2kyuu matome bunpou
2kyuu matome bunpou2kyuu matome bunpou
2kyuu matome bunpou
 
Masper - The India's Leading Home Furnishing Store
Masper - The India's Leading Home Furnishing StoreMasper - The India's Leading Home Furnishing Store
Masper - The India's Leading Home Furnishing Store
 
2015 하반기 AppJam
2015 하반기 AppJam 2015 하반기 AppJam
2015 하반기 AppJam
 
100 Years of Needlwork treated by Jennifer Hein: samplers, satin stitch, cros...
100 Years of Needlwork treated by Jennifer Hein: samplers, satin stitch, cros...100 Years of Needlwork treated by Jennifer Hein: samplers, satin stitch, cros...
100 Years of Needlwork treated by Jennifer Hein: samplers, satin stitch, cros...
 
Verslag humanistisch lab 20 oktober HV MiddenHolland
Verslag humanistisch lab 20 oktober HV MiddenHollandVerslag humanistisch lab 20 oktober HV MiddenHolland
Verslag humanistisch lab 20 oktober HV MiddenHolland
 
Karya Ilmiah - Silvana Evi Linda
Karya Ilmiah - Silvana Evi LindaKarya Ilmiah - Silvana Evi Linda
Karya Ilmiah - Silvana Evi Linda
 
Uses & gratifications
Uses & gratifications Uses & gratifications
Uses & gratifications
 
тема 2 аппаратное обеспечение ит
тема 2   аппаратное обеспечение иттема 2   аппаратное обеспечение ит
тема 2 аппаратное обеспечение ит
 
Evaluation Question 1
Evaluation Question 1Evaluation Question 1
Evaluation Question 1
 
Dowcipy zdjeciowe
Dowcipy zdjecioweDowcipy zdjeciowe
Dowcipy zdjeciowe
 

Similar to Logic

Chapter 01 - p1.pdf
Chapter 01 - p1.pdfChapter 01 - p1.pdf
Chapter 01 - p1.pdfsmarwaneid
 
20220818151924_PPT01 - The Logic of Compound and Quantitative Statement.pptx
20220818151924_PPT01 - The Logic of Compound and Quantitative Statement.pptx20220818151924_PPT01 - The Logic of Compound and Quantitative Statement.pptx
20220818151924_PPT01 - The Logic of Compound and Quantitative Statement.pptxssuser92109d
 
logicproof-141212042039-conversion-gate01.pdf
logicproof-141212042039-conversion-gate01.pdflogicproof-141212042039-conversion-gate01.pdf
logicproof-141212042039-conversion-gate01.pdfPradeeshSAI
 
UGC NET Computer Science & Application book.pdf [Sample]
UGC NET Computer Science & Application book.pdf  [Sample]UGC NET Computer Science & Application book.pdf  [Sample]
UGC NET Computer Science & Application book.pdf [Sample]DIwakar Rajput
 
Drinkfromme.pptx
Drinkfromme.pptxDrinkfromme.pptx
Drinkfromme.pptxRavind8
 
Inductive reasoning & logic
Inductive reasoning & logicInductive reasoning & logic
Inductive reasoning & logictommy34g
 
Disrete mathematics and_its application_by_rosen _7th edition_lecture_1
Disrete mathematics and_its application_by_rosen _7th edition_lecture_1Disrete mathematics and_its application_by_rosen _7th edition_lecture_1
Disrete mathematics and_its application_by_rosen _7th edition_lecture_1taimoor iftikhar
 
[gaNita] 2. Propositional Equivalences [math.fsu.edu].pdf
[gaNita] 2. Propositional Equivalences [math.fsu.edu].pdf[gaNita] 2. Propositional Equivalences [math.fsu.edu].pdf
[gaNita] 2. Propositional Equivalences [math.fsu.edu].pdfrAjyarAjanItjJa
 
Logic in Computer Science Unit 2 (1).pptx
Logic in Computer Science Unit 2 (1).pptxLogic in Computer Science Unit 2 (1).pptx
Logic in Computer Science Unit 2 (1).pptxPriyalMayurManvar
 
Discrete mathematics Chapter1 presentation.ppt
Discrete mathematics Chapter1 presentation.pptDiscrete mathematics Chapter1 presentation.ppt
Discrete mathematics Chapter1 presentation.pptNandiniSR2
 
Discrete Structure vs Discrete Mathematics
Discrete Structure vs Discrete MathematicsDiscrete Structure vs Discrete Mathematics
Discrete Structure vs Discrete MathematicsAbdulRehman378540
 

Similar to Logic (20)

dm-logic.pdf
dm-logic.pdfdm-logic.pdf
dm-logic.pdf
 
Chapter 01 - p1.pdf
Chapter 01 - p1.pdfChapter 01 - p1.pdf
Chapter 01 - p1.pdf
 
20220818151924_PPT01 - The Logic of Compound and Quantitative Statement.pptx
20220818151924_PPT01 - The Logic of Compound and Quantitative Statement.pptx20220818151924_PPT01 - The Logic of Compound and Quantitative Statement.pptx
20220818151924_PPT01 - The Logic of Compound and Quantitative Statement.pptx
 
logicproof-141212042039-conversion-gate01.pdf
logicproof-141212042039-conversion-gate01.pdflogicproof-141212042039-conversion-gate01.pdf
logicproof-141212042039-conversion-gate01.pdf
 
UGC NET Computer Science & Application book.pdf [Sample]
UGC NET Computer Science & Application book.pdf  [Sample]UGC NET Computer Science & Application book.pdf  [Sample]
UGC NET Computer Science & Application book.pdf [Sample]
 
Drinkfromme.pptx
Drinkfromme.pptxDrinkfromme.pptx
Drinkfromme.pptx
 
Per3 logika&pembuktian
Per3 logika&pembuktianPer3 logika&pembuktian
Per3 logika&pembuktian
 
Chapter 1
Chapter 1Chapter 1
Chapter 1
 
Chapter 1
Chapter 1Chapter 1
Chapter 1
 
Chapter1p1.pdf
Chapter1p1.pdfChapter1p1.pdf
Chapter1p1.pdf
 
Inductive reasoning & logic
Inductive reasoning & logicInductive reasoning & logic
Inductive reasoning & logic
 
Slide subtopic 1
Slide subtopic 1Slide subtopic 1
Slide subtopic 1
 
Disrete mathematics and_its application_by_rosen _7th edition_lecture_1
Disrete mathematics and_its application_by_rosen _7th edition_lecture_1Disrete mathematics and_its application_by_rosen _7th edition_lecture_1
Disrete mathematics and_its application_by_rosen _7th edition_lecture_1
 
L01.ppt
L01.pptL01.ppt
L01.ppt
 
[gaNita] 2. Propositional Equivalences [math.fsu.edu].pdf
[gaNita] 2. Propositional Equivalences [math.fsu.edu].pdf[gaNita] 2. Propositional Equivalences [math.fsu.edu].pdf
[gaNita] 2. Propositional Equivalences [math.fsu.edu].pdf
 
Logic in Computer Science Unit 2 (1).pptx
Logic in Computer Science Unit 2 (1).pptxLogic in Computer Science Unit 2 (1).pptx
Logic in Computer Science Unit 2 (1).pptx
 
Discrete mathematics Chapter1 presentation.ppt
Discrete mathematics Chapter1 presentation.pptDiscrete mathematics Chapter1 presentation.ppt
Discrete mathematics Chapter1 presentation.ppt
 
DS Lecture 2.ppt
DS Lecture 2.pptDS Lecture 2.ppt
DS Lecture 2.ppt
 
Chapter1p1 2.pptx
Chapter1p1 2.pptxChapter1p1 2.pptx
Chapter1p1 2.pptx
 
Discrete Structure vs Discrete Mathematics
Discrete Structure vs Discrete MathematicsDiscrete Structure vs Discrete Mathematics
Discrete Structure vs Discrete Mathematics
 

More from Jeane Paguio

Slideshare Project Proposal
Slideshare Project ProposalSlideshare Project Proposal
Slideshare Project ProposalJeane Paguio
 
Technical writing i
Technical writing iTechnical writing i
Technical writing iJeane Paguio
 
Future of systems analysis
Future of systems analysisFuture of systems analysis
Future of systems analysisJeane Paguio
 

More from Jeane Paguio (6)

Slideshare Project Proposal
Slideshare Project ProposalSlideshare Project Proposal
Slideshare Project Proposal
 
Technical writing i
Technical writing iTechnical writing i
Technical writing i
 
UCL of Slideshare
UCL of SlideshareUCL of Slideshare
UCL of Slideshare
 
Graph theory
Graph theoryGraph theory
Graph theory
 
Boolean
BooleanBoolean
Boolean
 
Future of systems analysis
Future of systems analysisFuture of systems analysis
Future of systems analysis
 

Recently uploaded

ENGLISH6-Q4-W3.pptxqurter our high choom
ENGLISH6-Q4-W3.pptxqurter our high choomENGLISH6-Q4-W3.pptxqurter our high choom
ENGLISH6-Q4-W3.pptxqurter our high choomnelietumpap1
 
Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)Mark Reed
 
DATA STRUCTURE AND ALGORITHM for beginners
DATA STRUCTURE AND ALGORITHM for beginnersDATA STRUCTURE AND ALGORITHM for beginners
DATA STRUCTURE AND ALGORITHM for beginnersSabitha Banu
 
Judging the Relevance and worth of ideas part 2.pptx
Judging the Relevance  and worth of ideas part 2.pptxJudging the Relevance  and worth of ideas part 2.pptx
Judging the Relevance and worth of ideas part 2.pptxSherlyMaeNeri
 
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17Celine George
 
Choosing the Right CBSE School A Comprehensive Guide for Parents
Choosing the Right CBSE School A Comprehensive Guide for ParentsChoosing the Right CBSE School A Comprehensive Guide for Parents
Choosing the Right CBSE School A Comprehensive Guide for Parentsnavabharathschool99
 
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITY
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITYISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITY
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITYKayeClaireEstoconing
 
ECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptx
ECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptxECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptx
ECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptxiammrhaywood
 
Barangay Council for the Protection of Children (BCPC) Orientation.pptx
Barangay Council for the Protection of Children (BCPC) Orientation.pptxBarangay Council for the Protection of Children (BCPC) Orientation.pptx
Barangay Council for the Protection of Children (BCPC) Orientation.pptxCarlos105
 
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPTECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPTiammrhaywood
 
Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17Celine George
 
Science 7 Quarter 4 Module 2: Natural Resources.pptx
Science 7 Quarter 4 Module 2: Natural Resources.pptxScience 7 Quarter 4 Module 2: Natural Resources.pptx
Science 7 Quarter 4 Module 2: Natural Resources.pptxMaryGraceBautista27
 
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdfInclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdfTechSoup
 
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdfLike-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdfMr Bounab Samir
 
Difference Between Search & Browse Methods in Odoo 17
Difference Between Search & Browse Methods in Odoo 17Difference Between Search & Browse Methods in Odoo 17
Difference Between Search & Browse Methods in Odoo 17Celine George
 
Q4 English4 Week3 PPT Melcnmg-based.pptx
Q4 English4 Week3 PPT Melcnmg-based.pptxQ4 English4 Week3 PPT Melcnmg-based.pptx
Q4 English4 Week3 PPT Melcnmg-based.pptxnelietumpap1
 
GRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTS
GRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTSGRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTS
GRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTSJoshuaGantuangco2
 
Roles & Responsibilities in Pharmacovigilance
Roles & Responsibilities in PharmacovigilanceRoles & Responsibilities in Pharmacovigilance
Roles & Responsibilities in PharmacovigilanceSamikshaHamane
 

Recently uploaded (20)

ENGLISH6-Q4-W3.pptxqurter our high choom
ENGLISH6-Q4-W3.pptxqurter our high choomENGLISH6-Q4-W3.pptxqurter our high choom
ENGLISH6-Q4-W3.pptxqurter our high choom
 
Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)
 
DATA STRUCTURE AND ALGORITHM for beginners
DATA STRUCTURE AND ALGORITHM for beginnersDATA STRUCTURE AND ALGORITHM for beginners
DATA STRUCTURE AND ALGORITHM for beginners
 
Judging the Relevance and worth of ideas part 2.pptx
Judging the Relevance  and worth of ideas part 2.pptxJudging the Relevance  and worth of ideas part 2.pptx
Judging the Relevance and worth of ideas part 2.pptx
 
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
 
Choosing the Right CBSE School A Comprehensive Guide for Parents
Choosing the Right CBSE School A Comprehensive Guide for ParentsChoosing the Right CBSE School A Comprehensive Guide for Parents
Choosing the Right CBSE School A Comprehensive Guide for Parents
 
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITY
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITYISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITY
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITY
 
Raw materials used in Herbal Cosmetics.pptx
Raw materials used in Herbal Cosmetics.pptxRaw materials used in Herbal Cosmetics.pptx
Raw materials used in Herbal Cosmetics.pptx
 
ECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptx
ECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptxECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptx
ECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptx
 
Barangay Council for the Protection of Children (BCPC) Orientation.pptx
Barangay Council for the Protection of Children (BCPC) Orientation.pptxBarangay Council for the Protection of Children (BCPC) Orientation.pptx
Barangay Council for the Protection of Children (BCPC) Orientation.pptx
 
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPTECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
 
LEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptx
LEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptxLEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptx
LEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptx
 
Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17
 
Science 7 Quarter 4 Module 2: Natural Resources.pptx
Science 7 Quarter 4 Module 2: Natural Resources.pptxScience 7 Quarter 4 Module 2: Natural Resources.pptx
Science 7 Quarter 4 Module 2: Natural Resources.pptx
 
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdfInclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
 
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdfLike-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
 
Difference Between Search & Browse Methods in Odoo 17
Difference Between Search & Browse Methods in Odoo 17Difference Between Search & Browse Methods in Odoo 17
Difference Between Search & Browse Methods in Odoo 17
 
Q4 English4 Week3 PPT Melcnmg-based.pptx
Q4 English4 Week3 PPT Melcnmg-based.pptxQ4 English4 Week3 PPT Melcnmg-based.pptx
Q4 English4 Week3 PPT Melcnmg-based.pptx
 
GRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTS
GRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTSGRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTS
GRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTS
 
Roles & Responsibilities in Pharmacovigilance
Roles & Responsibilities in PharmacovigilanceRoles & Responsibilities in Pharmacovigilance
Roles & Responsibilities in Pharmacovigilance
 

Logic

  • 1. Logic Is the study of reasoning; it is specifically concerned with whether reasoning is correct. It focuses on the relationship among statements as opposed to the content of any particular statement. Proposition A sentence that is either true or false, but not both. It is typically expressed as a declarative sentence (as opposed to a question, command, etc.). Propositions are the basic building blocks of any theory of logic.
  • 2. Which sentences are proposition? 1. The only positive integers that divide 7 are 1 and 7 itself. 2. Alfred Hitchcock won an Academy Award in 1940 for directing “Rebecca.” 3. For every positive integer n, there is a prime number larger than n. 4. Earth is the only planet in the universe that contains life. 5. Buy two tickets to the “Never Say Never” concert.
  • 3. We will use variables, such as p, q, and r, to represent propositions. Let p and q be propositions. The conjunction of p and q, denoted p ʌ q, is the proposition p and q The disjunction of p and q, denoted p v q, is the proposition p or q
  • 4. example: if p: It is raining, q: It is cold, then the conjunction of p and q is p ʌ q: It is raining and It is cold. The disjunction of p and q is p v q: It is raining or it is cold. The inclusive-or of propositions p and q is true if both p and q are true.
  • 5. The exclusive-or that defines p exor q to be false if both p and q are true. Exclusive-or p T T F F q T F T F p exor q F T T F
  • 6. Truth tables The truth values of propositions such as conjunctions and disjunctions can be described by truth tables. Conjunction Disjunction pʌ q T T T T F F F T F F F F The binary operator on a set elements in X an element of X. p q p T T F F q T F T F pvq T T T F X assigns to each pair of ʌ and v are both binary operator on propositions.
  • 7. The negation of p, denoted by ¬p, is the proposition not p example: p: Paris is the capital of England. ¬p: Paris is not the capital of England. Negation p T F ¬p F T A unary operator on a set X assigns to each element in X an element of X. ¬ is a unary operator on propositions.
  • 8. Operator Precedence In expressions involving some or all of the operators ¬, ʌ, and v, in the absence of parentheses, we first evaluate ¬, then ʌ, and then v. example: Given that proposition p is false, proposition q is true, and proposition r is false, determine whether the proposition ¬p v q ʌ r is true or false.
  • 9. Exercises: I.Given that proposition p is false, proposition q is true, and proposition r is false, determine whether each proposition is true or false. 1. p v q 3. ¬p v q 5. ¬(p v q) ʌ (¬p v r) II. 2. ¬p v ¬q 4. ¬p v ¬(q ʌ r) 6. (p v ¬r) ʌ ¬[(q v r) v ¬(r v p)] Write the truth table of each proposition. 7. p ʌ ¬q 9. (p v q) ʌ ¬p 11. (p ʌ q) v (¬p v q) 13. ¬(p ʌ q) v (¬q v r) 8. (¬p v ¬q) v p 10. (p ʌ q) ʌ ¬p 12. ¬(p ʌ q) v (r ʌ ¬p) 14. (p ʌ q) ʌ ¬(r v p)
  • 10. III. Represent the proposition symbolically by letting p: There is a storm. q: It is raining. 15. There is no storm. 16. There is a storm and it is raining. 17. There is a storm, and it is not raining. 18. There is no storm and it is not raining. 19. Either there is a storm or it is raining. 20. Either there is a storm or it is raining, but there is no storm.
  • 11. Conditionals The implication (or conditional) p → q, is the proposition “if p, then q”, that is false when p is true and q is false and true otherwise. In this implication p is called the hypothesis (or antecedent or premise) and q is called the conclusion (or consequence). Implication p T T F F q T F T F p →q T F T T
  • 12. Other ways of expressing implication: “p implies q” “p is sufficient for q” “when p then q” “q if p” “if p, q” “q whenever p” “p only if q” “q is necessary for p” Example: Rewrite the given implication in other forms. If it snows, then the streets are slick. 1.The streets are slick if it snows. 2.Snowy weather implies the streets are slick. 3.When it snows, then the streets are slick. 4.It snows only if the streets are slick.
  • 13. Related Conditionals The converse of the implication “if p, then q”, is the implication “if q, then p.” That is, the converse of p→q is q→p. The inverse of the implication “if p, then q”, is the implication “if not p, then not q” that results when p and q are replaced by their negations; that is, the inverse of p→q is ¬p→¬q The contrapositive of the implication “if p, then q”, is the implication “if not q, then not p.” That is, the contrapositive of p→q is ¬q→¬p.
  • 14. Example: Find the converse, inverse, contrapositive of the given conditional. and If it rains, then I buy a new umbrella. Converse: If I buy a new umbrella, then it is raining. Inverse: If it does not rain, then I do not buy a new umbrella. Contrapositive: If I do not buy a new umbrella, then it is not raining.
  • 15. The biconditional p↔q is the proposition that is true when p and q have the same truth values and is false otherwise. “p if and only if q” “p is necessary and sufficient for q” “if p then q, and conversely” Biconditional p T T F F q T F T F p ↔q T F F T
  • 16. Propositional Equivalences Tautology is a compound proposition that is always true, no matter what the truth values of the propositions that occur in it. Contradiction is a compound proposition that is always false. Contingency is a proposition that is neither a tautology nor a contradiction. Example of Tautology and Contradiction p T F ¬p F T p v ¬p T T p ʌ ¬p F F
  • 17. Logical Equivalence Compound propositions that have the same truth values in all positive cases are called logically equivalent. The propositions p and q are called logically equivalent if p↔q is a tautology. The notation p⇔q denotes that p and q are logically equivalent. Ex. Show that the propositions p v (q ʌ r) and (p v q) ʌ (p v r) are logically equivalent. Table
  • 18. Exercises: I.Let A: “It is snowing” B: “The roofs are white” C: “The streets are slick” D: “The trees are covered with ice” Write the following in symbolic notation. 1. If it is snowing, then the trees are covered with ice. 2. If it is not snowing, then the roofs are not white. 3. If the streets are not slick, then it is not snowing. 4. If the streets are slick, then the trees are not covered with ice.
  • 19. II. State the converse, inverse, and contrapositive of each of the following conditionals. 1. If a triangle is a right triangle, then one angle has a measure of 900. 2. If a number is a prime, then it is odd. 3. If two lines are parallel, then the alternate interior angles are equal. 4. If Joyce is smiling, then she is happy. III. Using truth tables, determine whether the following pairs of statements are equivalent. 1. p v q; ¬p→q 2. ¬(p ʌ q); ¬p v ¬q 3. ¬(p v q); ¬p ʌ ¬q 4. p→q; ¬p→¬q
  • 20. IV. Using truth tables, determine if the argument is tautology, contradiction or contingency. 1.¬p → (p→q) 2.[p ʌ (p→q)] → ¬q 3.p ʌ ¬p 4.[(p→q) ʌ (q→r)] → (p→r) 5.[(p v q) ʌ (p→r) ʌ (q→r)] → r 6.(p→q) ʌ (p ʌ ¬q)
  • 21. Quantifiers, Venn Diagrams, and Valid Arguments Quantifiers give information about “how many” in the statements in which they occur. ex. all, some, no Quantified Statements are statements involving quantifiers. ex. Some women have red hair. All bananas are yellow. No professors are bald-headed.
  • 22. Universal Quantifiers ( ∀ ) The words all, every, and each are called universal quantifiers because when these words are added to an open sentence to make it a statement, the sentence must be true in all possible instances in order for the statement to be true. ∀xP(x) “for all x P(x)” or “for every x P(x) examples: All prime numbers greater than 2 are odd. Every automobile pollutes the atmosphere. For each number x, x + 3 = 3 + x.
  • 23. Existential Quantifiers ( ∃ ) Other quantified statements are intended to indicate that there exists at least one case in which the statement is true. Such statements generally involve one of the existential quantifiers: some, there exist, or there exists at least one. ∃xP(x) “there is an x such that P(x)” or “there is at least one x such that P(x) examples: Some men have black hair. There exist students who do not work hard. There exists at least one student who does not work hard.
  • 24. Venn Diagrams A diagram in which the interiors of simple closed curves such as circles are used to represent collections of objects (or sets). It provide geometrical representations of the relationships indicated by statements involving quantifiers. ex. Draw Venn diagrams for the statements. 1.All dogs (D) are animals (A). 2.Some kindergarten students (K) are able to read (R). 3.No cat (C) is a dog (D).
  • 25. Valid Arguments The argument is valid if in all cases where the premises are true, the conclusion is true. Ex. Consider the following two arguments. Use Venn diagrams to determine whether either is valid. a)All leghorns are chickens. All chickens are fowls. Therefore, all leghorns are fowls. b)All leghorns are chickens. All chickens are fowls. Therefore, all fowls are leghorns.
  • 26. If, in some case, the conjunction of the premises is true and the conclusion is false, then the argument is invalid. Invalid arguments are sometimes called fallacies. Theorem is a statement that can shown to be true. Proof is a sequence of statements that form an argument to demonstrate that a theorem is true. Axioms or Postulates are the underlying assumptions about mathematical structures, the hypotheses of the theorem to be proved, and previously proved theorem.
  • 27. Lemma is a simple theorem used in the proof of other theorems. Corollary is a proposition that can be established directly from a theorem that has been proved. Conjecture is a statement whose truth value is unknown. When a proof of a conjecture is found, the conjecture becomes a theorem. Many times conjectures are shown to be false, so they are not theorems. Rules of Inference which are means used to draw conclusions from other assertions, tie together the steps of a proof.
  • 28. Rules of Inference Rule Tautology Name p ∴p∨q p → ( p ∨ q) Addition p∧q ∴p ( p ∧ q) → p Simplification p q ∴p∧q ( ( p) ∧ ( q) ) → ( p ∧ q) Conjuction
  • 29. Rule p p→q ∴q ¬q p→q ∴ ¬p p→q q→r ∴p→r p∨q ¬p ∴q Tautology [ p ∧ ( p → q)] → q [ ¬q ∧ ( p → q ) ] → ¬p Name Modus ponens Modus tollens [( p → q) ∧ ( q → r )] → ( p → r ) Hypothetical syllogisms or chain rule [ ( p ∨ q ) ∧ ¬p ] → q Disjunctive syllogisms